Update app.py

app.py

@@ -23,7 +23,7 @@ UPLOAD_FOLDER = '/tmp/uploads'
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 
 # Label klasifikasi tanah
-class_labels = ['Black Soil', 'Cinder Soil', 'Laterite Soil', 'Peat Soil', 'Yellow Soil']
+class_labels = ['Red Soil', 'Alluvial Soil', 'Clay Soil', 'Black Soil']
 num_classes = len(class_labels)
 
 # Load model ViT (PyTorch)
@@ -37,7 +37,7 @@ crop_model = joblib.load("models/model_random_forest.joblib")
 crop_model_label = joblib.load("models/label_encoder.joblib")
 
 # Load data referensi
-soil_df = pd.read_csv("data/soil_data_with_class.csv")
+soil_df = pd.read_csv("data/soil_data (2).csv")
 agri_df = pd.read_csv("data/tips_menanam_dan_manfaat_tanaman.csv")
 
 # Fungsi preprocessing gambar (PyTorch style)
@@ -63,7 +63,7 @@ def predict_soil_type(img_path):
 
 # Hitung jarak dengan haversine
 def find_nearest_soil_data_weighted(soil_type, lat, lon, n_points=1):
-    filtered = soil_df[soil_df['Class_Name'] == soil_type].copy()
+    filtered = soil_df[soil_df['Soil_Type'] == soil_type].copy()
     if filtered.empty:
         return None